Method for analyzing activity in a signal

ABSTRACT

A method for analyzing a signal. The signal is passed through a noise filter to generate a filtered signal. The filtered signal is passed through an adaptive pattern recognition unit. At the adaptive pattern recognition unit, a pattern that is a function of the filtered signal is predicted. A mathematical deviation in the filtered signal is detected by comparing the filtered signal with the predicted pattern. The detected mathematical deviation is flagged. The filtered signal is passed through at least two pattern recognition weighing units. At each pattern recognition weighing unit, the filtered signal at about the detected mathematical deviation is matched with a collection of stored patterns. A weight is assigned to each stored pattern. Each stored pattern and the assigned weight of the stored pattern are input to an expert system ranking unit. At the expert system ranking unit, one of the stored patterns is selected by applying a predefined set of generalized rules. Additionally, the method may determine if activity in a signal is individual event activity. A moment of chaos in the signal may also be determined.

This application is a division of application Ser. No. 07/847,776, whichwas filed on Mar. 6, 1992 now U.S. Pat. No. 5,402,520.

BACKGROUND OF THE INVENTION Field of the Invention

This invention relates to apparatus and method incorporating a neuralnetwork for retrieving signals embedded in noise and analyzing theretrieved signals. In a first stage, the noise is filtered out of theinput signal to retrieve a filtered data signal. In a second stage, thefiltered data signals are analyzed to determine their behavior. In athird stage, the significance of the behavior of the signals isdetermined. The apparatus and method of the invention are usable in avariety of applications where signals are embedded in noise, including,without limitation, earthquake and other seismic data, stock market data( including stocks, options, futures, commodities, currencies, indices,foreign government and municipal securities, bonds, treasury bills,foreign exchange rates, interest rates), high definition televisionsignals, radar, sonar, ultrasound imaging, edge detection, detection ofpresence of certain elements, such as lead, from X-rays of the bone, andthe like.

The present invention is preferably implemented using a computer and adigitized data processor. In a preferred embodiment of the presentinvention, the computer receives stock market data input signals, learnsthe pattern of signals to predict future behavior, identifies deviationsfrom the learned patterns, analyzes the deviations in view of previouslystored deyiation patterns of stock market behavior, and ultimatelyidentifies the meaning of the deviations, such as profitable buy/sellselections in the stock market.

The present invention includes a filter to filter out noise and focus onwhat is essential in the market. The filter separates from the chaos ofthe market those activities which indicate a trend. The presentinvention further includes a neural network that has learned and willcontinue to learn stock market data behavior. The neural network applieslearned patterns to incoming data to identify and recommend possiblehighly profitable buy/sell selections that may or may not follow thelearned patterns. The present invention includes an expert system whichprioritizes the recommended buy/sell selections.

It is useful to analyze signals embedded in noise, such as stock, usinga neural network for the following reasons: (1) unlike traditionalexpert systems where knowledge is represented explicitly in the form ofrules, neural networks can learn from examples; (2) neural networks havethe ability to recognize patterns in data and classify incoming datainto previously learned examples or, trained pattern sets; and (3)neural networks are known for their ability to learn from experience, togeneralize, and to recognize and predict patterns.

The neural network portion of the apparatus and method of the presentinvention includes a self adaptive and variant error ("SAVE") filter.The SAVE filter views the incoming data prior to the onset of a signalof potential interest from a simple quadratic error filter. Once thesignal of potential interest impinges the system of the presentinvention, as identified by the error:

1. the coefficients related to the data prior to the signal are"frozen";

2. the filter becomes an adaptive autoregressive moving average ("ARMA")filter; and

3. the usual nonlinearities of an adaptive ARMA are avoided, since the"frozen" coefficients retain the mathematics within the quadraticregime.

The SAVE filter provides determination of:

1. the optimum approach to freezing the coefficients;

2. the optimum filter lengths for each portion of the filter;

3. the optimum adaptation time associated with each portion of the SAVEfilter; and

4. improved signal detection, identification, and pattern recognitionafter determining (1) through (3).

SUMMARY OF THE INVENTION

The invention will be summarized with reference to FIG. 1. Let S(0,n)represent the array of values of a data signal from S(n), the locationor moment of interest such as the present place or time, to S(0), thevalue of the data signal n intervals away in location or time. Forexample, S(0,n) could be stock price as a function of time and the timeinterval could be anything from the interval of each trade to a tradingday. For simplicity, such an array S(0,n) is represented by a datasignal S in the drawings.

In a preferred embodiment, one or more arrays of related or discriminantsignals D_(n) (0,n) may also be processed by the system. For example, D₁(0,n) could be stock volume as a function of time. For simplicity, suchan array D_(n) (0,n) is represented by a discriminant signal D_(n) inthe drawings.

An array of noisy data signals S₀ (0,n) is input to a noise filter unit1 comprising at least one, preferably more than one and most preferablythree, noise filters F₁, F₂, F₃, . . . , F_(N) to generate acorresponding number of filtered data signal arrays S₁ (0,n), S₂ (0,n),S₃ (0,n), . . . , S_(N) (0,n). One or more arrays of noisy discriminantsignals D_(0n) (0,n) are similarly processed in the noise filter unit 1to generate filtered discriminant signal arrays D_(1n) (0,n), D_(2n)(0,n), D_(3n) (0,n), . . . , D_(Nn) (0,n).

The one or more filtered data signal arrays S₁ (0,n), S₂ (0,n), S₃(0,n), . . . , S_(N) (0,n) and the one or more filtered discriminantsignal arrays D_(1n) (0,n) , D_(2n) (0,n), D_(3n) (0,n), . . . , D_(Nn)(0,n) output from the noise filter unit 1 are then input to a "chaosidentifier" neural network comprising at least one and preferably morethan one processing stages.

In the first stage, the one or more filtered data signals are input toan adaptive pattern recognition unit 2 comprising at least one andpreferably two neural network systems F_(A), F_(B), . . . , F_(x), thatcan adaptively learn a pattern representing the filtered data signalsand Use this learned pattern to predict the value of future datasignals. Each neural network system F_(A), F_(B), . . . F_(X), learns apattern P_(1A) (O,n--1), . . . , P_(NA) (O,n--1) , P_(1B) (O,n--1), . .. , P_(NB) (0,n--1), . . . , P_(1X) (O,n--1), . . . , P_(NX) (0,n--1)that is a function of each of the filtered data signals S₁ (O,n--1), S₂(0,n--1), S₃ (0,n--1), . . . , S_(N) (0,n--1) . These patterns P_(1A)(0,n--1), . . . , P_(NX) (O,n--1) are stored in memory and optionallymay be output to an output device.

The at least one learned patterns output by the pattern recognition unit2 of the chaos identifier neural network, namely patterns P_(1A)(0,n--1), . . . , P_(NX) (0,n--1), together with the one or morefiltered data signal arrays S₁ (0,n), S₂ (0,n), S₃ (0,n), . . . , S_(N)(0,n) and the one or more filtered discriminant signal arrays D_(1n)(0,n), D_(2n) (0,n), D_(3n) (0,n), . . . , D_(Nn) (0,n), are then inputto an event identifying unit 3, which includes the second and thirdstages of the chaos identifier neural network.

In the second stage of the chaos identifier, the event identifying unit3 calculates the errors, ε_(1A), . . . , ε_(NX), between the actualvalues of S₁ (n), . . . , S_(N) (n) and the values, S₁ (n), . . . ,S_(N) (n) which are predicted from the patterns P_(1A) (0,n--1), . . . ,P_(NX) (0,n--1). If any of the errors exceeds a small specified amountδ, say δ_(1A) >δ, then an event is flagged and a flag F may optionallybe output to an output device.

In the third stage of the chaos identifier, the event identifying unit 3ascertains the exact moment of chaos, t_(c). While the discriminantsignals D_(Nn) can be processed as a signal and evaluated for theirerror, in this preferred embodiment, the discriminants are not subjectedto error evaluation, rather they are used as an aid in choosing theexact moment of chaos. The resultant signals are used to form patternsR_(N).sbsb.i. These patterns R_(N).sbsb.i, if deemed unique or highlyrepresentative, optionally may be output to an output device. Theserepresentative patterns R_(N).sbsb.i are preferably stored in apre-existing vault of patterns R for later use.

The filtered data signals S_(1A), . . . , S_(NX), each with itsrespective identified moment of chaos, t_(c).sbsb.1A, . . . ,t_(c).sbsb.NX, the pre-moment of chaos patterns P_(NX), therepresentative patterns R_(N).sbsb.i with the enhanced post-moment ofchaos data signals, and the discriminant signals D_(1n), . . . , D_(Nn)are sent to a pattern recognition system unit 4. The pattern recognitionsystem learns adaptively from experience and separates variousdiscriminants from the unrelated or attenuating factors. The unit 4consists of at least one, preferably more than one, and most preferablythree, pattern recognition systems, at least one of which generalizesthe signal pattern R_(N), for i>0, in a pattern vault and chooses thestored pattern R_(NX) which most easily generalizes to the signalpattern R_(N).sbsb.i and assigns weights W_(NX) to the precision of thefit of R_(N).sbsb.i to R_(NX). The output from unit 4 comprises R_(NXM),W_(NXM).

The resultant chosen patterns R_(NXM) and their associated weightsW_(NXM) are input to an expert system unit 5. The expert system, using asee of rules, determines, given the R_(NXM) and W_(NXM), which signalsqualify for any specified category C_(f) and assigns a composite weightW_(f) to the category C_(f). These could be stocks for buy or sell, orlead in a bone from an x-ray fluorescence device. These outputsoptionally may be processed through a ranking unit which rearranges thecategories C_(f) according to R_(NXM), W_(NXM), and W_(f), using, butnot limited to, such a strategy as case-based reasoning. The results areoutput to an output device.

BRIEF DESCRIPTION OF THE DRAWINGS

Other objects, features, and advantages of the present invention willoccur to those skilled in the art from the following description of apreferred embodiment and the accompanying drawings, in which:

FIG. 1 is a schematic diagram illustrating the flow of signals throughthe apparatus and method of the present invention.

FIG. 2 is a schematic diagram of the noise filter unit 1 for filteringout noise to retrieve signals.

FIGS. 3A through 3C are circuit diagrams illustrating variousembodiments of an adaptive noise filter F₃.

FIG. 4 is a schematic diagram of the adaptive pattern recognition unit 2for learning a pattern of signals.

FIG. 5 is a circuit diagram illustrating one embodiment of an adaptivepattern recognition filter F_(A) ("TRAPS").

FIG. 6 is a circuit diagram illustrating one embodiment of an adaptivepattern recognition filter F_(B) ("HIPS").

FIG. 7 is a comparison of a pattern learned by filter F_(A) and actualnoiseless signals.

FIG. 8 is a comparison of a pattern learned by filter F_(A) and actualnoisy signals.

FIG. 9 is an example of a graphical output for an actual stock, asanalyzed by the method and apparatus of the present invention, with iconflags denoting times to "buy" (uptrends) and time to "sell" (downtrends)stock, the emphasis being the invention's perspective of the position totake on a specific stock of interest.

FIG. 10 is a schematic diagram illustrating the operations of the eventidentifying unit 3.

FIGS. 11A and 11B are depictions of hypothetical signals without andwith chaos, respectively.

FIGS. 12A through 12C are depictions of hypothetical signals andcorresponding patterns illustrating the interval of an event and theexact moment of chaos.

FIG. 13A is a circuit diagram of a pure autoregressive filter, whileFIGS. 13B and 13C are circuit diagrams illustrating the self-adaptiveand variant error ("SAVE") filter 10 for learning a new pattern ofdeviations of signals from a previously learned pattern.

FIGS. 14A and 14B are depictions of hypothetical signals without andwith noise, respectively.

FIG. 15 is a schematic diagram of the weighting unit 4 for weighting thefit of the calculated patterns to patterns stored in a data vault.

DESCRIPTION OF THE PREFERRED EMBODIMENT OF THE INVENTION

The preferred embodiment of the present invention will be described interms of a method and apparatus for analyzing stock market activities.The data signal input to the system S₀ will be stock price as a functionof time t. For the sake of simplicity, S₀ will designate S₀ (t), unlessotherwise specified. Because of the wide range of stock prices, theinput signals S₀ are preferably normalized prior to analysis.Normalization may be from $1-10 or $1-20 or any other desired range.

In a preferred optional embodiment, the trading volume of each stock ofinterest is monitored and analyzed in addition to the price of thatstock. Thus, the optional discriminant signal D₀ input to the systemwill be stock volume as a function of time t. Again, for simplicity, D₀will designate D₀ (t). While other discriminants, such as high/low,bid/ask, etc., can be used to improve results, just volume is used toillustrate the use of the discriminants. The discriminant(s) can also benormalized.

Noise Filtering

As shown in FIGS. 1 and 2, noisy data signals S₀ are input to a noisefilter unit 1 comprising at least one, preferably more than one, andmost preferably three noise filters F₁, F₂, F₃, . . . , F_(N) togenerate a corresponding number of filtered data signals S₁, S₂, S₃, . .. , S_(N).

The noise filter unit 1 in FIGS. 1 and 2 preferably comprises threefilters F₁, F₂, F₃ as shown in FIG. 2. The first filter F₁ is atransparent filter; it passes an input data signal S₀ unchanged foroutput as data signal S₁. The second filter F₂ is a non-adaptive noisefilter. Filter F₂ performs a pure 1 mathematical subtraction of thevariation of the relevant universe (in this example, it could be theaverage price change of a share of stock for the stock market or the DowJones Industrial Average (DJIA) or the average price of biotechnologystocks) from the variation in the input signal S₀ (an individual stockprice versus time). The output from filter F₂ is filtered data signalS₂. The third filter F₃ is an adaptive noise ("TRAPS") filter whichfilters out the noise of the universe itself (e.g., the stock market) toproduce a filtered data signal S₃. Filter F₃ is discussed in more detailbelow.

Like the price signal S₀, the volume signal D₀ is passed through thetransparent filter F₁ and the non-adaptive (pure mathematical) noisefilter F₂. When the discriminant is best represented by a pattern, asopposed to differences, it can The filtered volume signals D¹, D₂, D₃are then passed also be passed through the adaptive (TRAPS) noise filterF₃. directly to the event identifying unit 3.

The adaptive noise filter F₃ operates by comparing the desired responsesignal x(n) to the input signal x(n) and minimizing the error ε(n). Thefilter F₃ constantly adapts the desired response signal based on theinput signal, so that it constantly learns the signals and refines thealgorithm relating the response signal to the input signal. There are avariety of ways to obtain the desired response signal.

One such way, which is also referred to as statistical prediction, is touse a time-delayed input signal as the actual signal and the undelayedsignal as the desired response signal. In such a system, the inputsignal delayed by t time units is fed to the adaptive filter. Theundelayed input serves as the desired response for the adaptive filter.The filtered time-delayed input signals are compared to the desiredresponse signals to generate an error signal which, in turn, is appliedas input to the adaptive filter. The adaptive filter weights for thesignals adapt and converge to produce a best (weighted) LMS estimate ofthe present input signal. The (optional) weights providing the best LMSfit are copied into a slave filter. This slave filter then operates onthe undelayed input signal to produce an output signal which is aprediction of the input signal t time units into the future. An exampleof such a filter is illustrated in FIG. 3A. Further discussion of such afilter is found in B. Widrow & R. Winter, "Neural Nets for AdaptiveFiltering and Adaptive Pattern Recognition," Computer, March 1988, p.25, which is hereby incorporated by reference in its entirety.

An alternative method, known as noise cancellation, can be performed incases where a reference input containing noise n₁, which is correlatedwith the signal noise n₀, is available. In this example, the referenceinput might be the DJIA. In such a case, the primary input S₀, consistsof the "true" signal x plus noise n₀ and the reference input is thenoise n₁. The primary input is used as the desired signal, and thereference input is passed to the adaptive filter. The adaptive filterproduces a noise cancelling signal y which is applied to and compared tothe desired response signals to generate an error signal ε=x+n₀ --y,which is applied as input to the adaptive filter. Squaring and takingthe expectation value yields

    E(ε.sup.2)=E(x.sup.2)+E[(n.sub.0 --y)]+2E[x(n.sub.0 --y)](1)

Since x is uncorrelated with either n₀ or y, the last term drops out,leaving

    E(ε.sup.2)=E(x.sup.2)+E[(n.sub.0 --y).sup.2 ]      (2)

The adaptive filter is adapted to minimize the error without affectingthe signal, namely

    E.sub.min [ε.sup.2 ]=E[x.sup.2 ]+E.sub.min [(n.sub.0 --y.sup.2)](3)

An example of such a filter is illustrated in FIG. 3B. Furtherdiscussion of such a filter is found in B. widrow & R. Winter, "NeuralNets far Adaptive Filtering and Adaptive Pattern Recognition,"Computer,March 1988, p. 25, which is hereby incorporated by reference in itsentirety.

In the present example, where it is desired to filter out the noise ofthe universe, i.e. the stock market, then the "reference" noise signalmay be derived from a cumulation of individual noisy signals. Let

    X(n)=x(t.sub.n)=X(nΔt); n=0, 1,                      (4)

denote a variable excluding ambient noise, such as price of a givenstock, as a function of time. This variable will be modeled as theoutput of a linear recursive system. The variable will be subject toambient noise, from, in this example, the stock market. The ambientnoise sampled over time is assumed to be a transient autoregressivesequence. Analysis of the ambient noise requires the followingassumptions: (1) there is a dominant autoregressive (AR) component u(n);(2) this component is stationary or at worst slowly varying; and (3) anadaptive least-mean-square (LMS) filter with appropriate parametersconverges to (and tracks) this component.

First, let the actual measurements y(n) be written as

    y(n)=z(n)+u(n),                                            (5)

where u(n) satisfies the ambient equation L[u(n))]=w(n) and z(n) is theresidual component not satisfying this equation. L is the autoregressiveoperator acting on the vector of data values u(n) known at time n, andw(n) is the excitation variable, which, by assumption (2) above, will beassumed to be essentially white noise.

Next, define the error function ε(n)=L[y(n)]. Then

    ε(n)=x(n)-w(n),                                    (6)

where x(n)≡L[z(n)] is "transient" autoregressive. The adaptiveleast-mean-square (LMS) filter constructs a sequence of coefficients[α_(j) (n)] which converges to the true coefficients as n goes toinfinity. Then the predicted value can be written as ##EQU1## so that

    α.sub.j (n+1)=α.sub.j (n)+μδ(n)x(n-j), (8)

where δ(n)=x(n)-x(n), and the parameter μ controls the adaptation timeof the adaptive AR filter.

An example of such a filter, namely a Transient Acoustic ProcessingSystem ("TRAPS"), is illustrated in FIG. 3C. Further discussion of sucha filter is found in B. Schnitta-Israel & J. Sherry, "NuclearMonitoring, using a Hybrid Adaptive Processing System, Traps I," Feb.14, 1984, and in D. Fletcher, B. Schnitta-israel, & J. Dorman, "Hybridadaptive filtration for seismic event location," J. Acoust. Soc. Am.73(1), January 1983, p. 230, both of which are hereby incorporated byreference in their entireties.

The "Chaos Identifier" Neural Network

As shown in FIGS. 1 and 4, each of the at least one filtered datasignals S₁, S₂, S₃, . . . , S_(N) and their discriminants D₁, D₂, . . ., D_(N) from the at least one filters F₁, F₂, F₃, . . . , F_(N) are thenpassed to the adaptive pattern recognition unit 2 of the "chaosidentifier" neural network.

Learning Stocks

The first stage of the chaos identifier neural network is to "learn" thestocks using an adaptive pattern recognition filter. As shown in FIG. 4,the one or more filtered data signals are input to an adaptive patternrecognition unit 2 comprising at least one and preferably two neuralnetwork systems F_(A), F_(B), . . . , F_(X) that can adaptively learn apattern representing the filtered data signals and use this learnedpattern to predict the value of future data signals. Each neural networksystem F_(A), F_(B), . . . , F_(X) learns a pattern P_(1A), . . . ,P_(NA), P_(1B), . . . , P_(NB), . . . , P_(1X), . . . , P_(NX) that is afunction of each of the filtered data signals S₁, S₂, S₃, . . . , S_(N).These patterns P_(1A), . . . , P_(NX) are stored in a memory device (notshown) and optionally may be output to an output device (not shown).

The exemplary unit 2 shown in FIG. 4 has as a first adaptive patternrecognition filter F_(A) a hybrid AR filter, such as the "TRAPS" filterdiscussed above, and further illustrated in FIG. 5, in which the signalsare subject to signal enhancement, as follows.

Returning now to Equations (1) through (8), combining the modeling forthe individual stocks yields ##EQU2## where a_(m) (n+1)=a_(m)(n)+ηz(n-m)[z(n)-z(n)]. The error function ε can then be written as

    ε=z(n)-z(n)                                        (10)

The parameter η controls the adaptation times of the adaptive AR filter.

The independent adaptive filters for each stock m will converge to theambient operator L_(m) for that stock. The resulting noise sequencesw_(m) (n) will be independent, which permits maximum signal-to-noiseratio (SNR), When the sequences δ_(m) (n) are combined.

The second adaptive pattern recognition filter F_(B) of exemplary unit 2in FIG. 4 is an autoregressive moving average (ARMA) filter, such as the"HIPS" filter discussed in B. Schnitta-Israel, "Hybrid Image ProcessingSystem (HIPS) by Means of an Adaptive Autoregressive Moving AverageAlgorithm," IEEE Transactions on Sonics and Ultrasonics, SU-31(4), July1984, p. 391, which is hereby incorporated by reference in its entirety,and further illustrated in FIG. 6.

First consider an ARMA model in the following notation ##EQU3## in whichx(n) is the desired signal response series and W(n) is the excitationseries. Taking the z transform yields: ##EQU4## where A(z) and B(z) arep, q degree polynomials in z and W(z) is the transform of the seriesW(n). Thus, the observed series x(n) is considered to be the output of afilter with a transfer function H(z)=B(z)/A(z) and input W(n).

In contrast to an adaptive AR process in which the equations to besolved would be quadratic in nature and easily solvable, adaptive ARMAprocesses tend to be unstable. Thus, the adaptive ARMA requires a methodwhich remains stable within the process of solving the nonlinearequations without requiring excessive computer time.

Accordingly, the model of Equation (11) is rewritten in two parts asfollows: ##EQU5## and ##EQU6## The sum in the first part I (Equation(13)) can be thought of as representing an isolated stock, whereas thesum in the second part II (Equation (14)) can be thought of asrepresenting the trend of the market or, where the noise of the markethas been removed by filtering, as representing the trend of theindustry.

An estimate e(n) for the series e(n) is initially calculated using anadaptive filter, which constructs a sequence of coefficients [a_(j)(n)], which converge to the true coefficients as n goes to infinity.Thus ##EQU7## This then allows x(n) to be calculated as ##EQU8##allowing the estimate e(n) to be given by

    e(n)=x(n)-x(n)                                             (17)

Next, b_(i) and W(n) are estimated as those values b_(i) and W(n) whichminimize the total squared error, namely

    G.sub.n (b,W)=Z[F.sub.n (b,W)-e(n)].sup.2,                 (18)

subject to the constraints ##EQU9## where C is an arbitrary constraint,s is the number of unknown W, and ##EQU10##

Supposing that at time t_(n) =nΔt estimate e(n) is observed, then F canbe computed from a backward sum as follows: ##EQU11## where k=0, . . . ,N where n>N, b (i+1)=b_(i), and W(n--k--i)=W_(n--k--i) where n>k+i areFortran arrays representing the unknowns. These unknowns can berepresented at time n as ##EQU12## where 1≦m≦s+q+1, and q+1 is thenumber of unknown b.

Thus, the problem can be written at time n as ##EQU13## where λ_(p) areLagrange multipliers. The normal equations are ##EQU14## and

    R.sub.p (ξ)=Cδ.sub.po                             (25)

The normal equations must be solved for ξ (using the constraints to getλ_(p)), but they are nonlinear. Accordingly, the Gauss-Newton iterationmethod which ignores certain second derivatives is used to obtain:##EQU15## where i refers to the iteration number. The constraints arethe linearized to ##EQU16## These equations may be written in matrixform as ##EQU17## where ##EQU18## A single matrix equation may bewritten as ##EQU19## This matrix equation is solved, and the results areused to update the iterates:

    ξ.sup.n+1 =ξ.sup.n +Δξ, etc                 (30)

The HIPS adaptive pattern recognition filter F_(B) differs from theTRAPS adaptive pattern recognition filter F_(A) in that the HIPS filterF_(B) removes a second level of noise (if any) from the signal inlearning the patterns P_(NB) whereas the TRAPS filter F_(A) includesthis noise (if any) in learning the patterns P_(NA). For example, if thefirst level of noise eliminated in the filter unit 1 is the DJIA, thesecond level of noise removed by the HIPS filter F_(B) might be thetrend of biotechnology stocks relative to the DJIA, so that the patternsP_(NB) would focus on the individual stock, whereas the patterns P_(NA)would include the trend of biotechnology stocks relative, to the DJIA.

To reiterate, as illustrated in FIG. 4, each of the adaptive patternrecognition filters F_(A), F_(B), . . . F_(X) learns a pattern P_(NX)for each of the filtered signals S₁, S₂, S₃, . . . , S_(N). In thepreferred embodiment discussed herein, there would be six such patternsnamely, P_(1A), P_(2A), P_(3A), P_(1B), P_(2B), P_(3B). These patternsP_(NX) are stored in a memory device (not shown) for further use asdiscussed below. These patterns P_(NX) may optionally be output to anoutput device (not shown).

FIG. 7 shows a sample output from a TRAPS filter F_(A) for a noiselesssample showing actual stock price (solid line) and predicted stock price(dashed line) as a function of trading day (1-193). Note that the Curverepresenting the actual stock price begins on the first trading day,whereas the curve of predicted prices generated by the adaptive patternrecognition filter F_(A) is "turned on" on trading day 7. As seen inFIG. 7, the predicted curve quickly converges to the actual curve andcoalesces therewith around trading day 91. How quickly the predictedvalue converges to the actual value depends on the value of μ ofEquation (15).

FIG. 8 shows a sample output from a TRAPS filter F_(A) for a noisysample, again showing actual stock price (solid line) and "learned" orpredicted stock price (dashed line) as a function of trading day(1-193). Again, the curve representing the actual stock price begins onthe first trading day, whereas the curve of learned prices generated bythe adaptive pattern recognition filter F_(A) is turned on on tradingday 7. As seen in FIG. 8, the learned curve converges to the actualnoisy curve more slowly than in the case of the noiseless curve of FIG.7.

FIG. 9 illustrates an optional "buy/sell" flag output from the adaptivepattern recognition unit 2 from an actual stock result. Using thelearned pattern P_(NX), unit 2 continues to track incoming filteredsignals S_(N) and to update the patterns P_(NX). Based cn the updatedlearned patterns P_(NX) and the incoming signals S_(N), unit 2 candetermine when a stock price is in an uptrend and generate a "buy alert"flag with an actual "buy" signal given when three or more alerts occur(around Feb. 8 to June), as well as when a stock price is in a downtrendand generate similar "sell alert" flags and "sell" flags (around June tomid-October).

Identifying an Event

The neural network has learned and will continue to learn stockbehavior, so that it can apply learned patterns P_(NX) to incomingfiltered signals S_(N). Once the behavior of a stock has been learned inthe system, the neural network can detect deviations of the incomingfiltered signals S_(N) from the learned patterns P_(NX) and thusidentify an "event" for the stock. An "event" is a deviation from thestock's recent past behavior, including both up and down trends from itsoriginal up and down trend. As shown in FIG. 10, the event identifyingunit 3 includes the second and third stages of the chaos identifierneural network.

The at least one learned patterns P_(1A), . . . , P_(NX) output by thepattern recognition unit 2 of the chaos identifier neural networktogether with the filtered data signals S₁, . . . S_(N) and the filtereddiscriminant signals D₁, . . . D_(N), are input to the event identifyingunit 3.

In the second stage of the chaos identifier, the event identifying unit3 calculates the errors, ε_(1A), . . . , ε_(NX), between the actualvalues of S₁ (n), . . . , S_(N) (n) and the estimated values, S₁ (n), .. . , S_(N) (n) , which are predicted from the patterns P_(1A) (Δ,n--1),. . . , P_(NX) (0,n--1). See Equations (9) and (10). If any of theerrors exceeds a small specified amount δ, say ε_(1A) >δ, then an eventis flagged and a flag F may optionally be output to an output device.FIG. 9 is an example of an output of flags that is presentedgraphically. In the example of FIG. 9, the value of δ was set very smallso that flags were generated almost daily to indicate a change in stockprice and whether the change was positive or negative. Consequently,three flags were required to indicate a "change in buy/sell position"event.

In the third stage Of the chaos identifier, the event identifying unit 3ascertains the exact moment of chaos, t_(c). See FIG. 11B whichillustrates chaotic behavior relative to FIG. 11A. As illustrated inFIGS. 12A and 12B, after detecting a first significant (non-negligible)error ε between the actual signals S_(NX) and the projected signalsS_(NX) based on the learned patterns P_(NX), the event identifying unit3 continues to detect additional non-negligible errors e defining aninterval of event before being "certain" that an event has occurred.When the unit 3 is certain that an event has occurred, it determines themoment of chaos t_(c) based on the first detected non-negligible errorε.

While the discriminant signals D_(N) are not subjected to errorevaluation in this preferred embodiment, they are used as an aid inchoosing the exact moment of chaos. The incoming filtered volume signalsD₁, D₂ are compared to the previous volume signals to ascertain whetherand when a marked change in volume occurred. The time of the markedchange in volume may then be used to determine the exact moment ofchaos, namely time t_(c), as shown in FIG. 12C. The event identifyingunit 3 freezes the patterns P_(NX) at P_(NX) (t_(c).sbsb.NX) andtriggers the neural network to switch from the HIPS filter to a SelfAdaptive and Variant Error ("SAVE") filter as discussed below.

Learning an Enhanced Pattern of Deviation

After an event has been determined, the data signals are passed throughthe SAVE filter to generate an enhanced signal, representativecoefficients, and their respective polynomial expansion.

For the sake of illustration, consider a data signal array S which hasbeen passed through a transparent filter F₁ to produce an unchanged datasignal S₁. Thus, S₁ will be a noisy signal, as illustrated in FIG. 14B,for example. Now, consider that the signal array S₁ from filter F₁ hasbeen passed through both the TRAPS filter F_(A) and the HIPS filterF_(B) to generate patterns P_(1A) and P_(1B). Next, consider that theevent identifying unit 3 has determined that an event has occurred attime t_(c).sbsb.1A (based on the output of the TRAPS filter F_(A))=timet_(c).sbsb.1B (based on the output of the HIPS filter F_(B))=t_(c) inFIG. 14B. And further consider that the time t was identified as"certain" as of time t₂ based on the data received from time t₀ to timet₂. The output from the TRAPS filter based on the data signal array fromtime t₀ to time t₂ will be preserved for further analysis, and thesignal array S₁ from time t₀ to time t₂ will be reprocessed by theSAVE=filter (rather than the HIPS filter) and the output of the SAVEfilter will be used for further analysis.

The SAVE filter operates on the principle that the present value of apolynomial may be computed recursively from previous values. Letting Z⁻¹represent the delay operator, FIG. 13A shows an example where, withknowledge of two weighting coefficients or poles (b₁ and b₂ shown inFIG. 13A) and two noise-free samples (x_(k-1) and x_(k--2) also shown inFIG. 13A), it is possible to perfectly predict and specify the processfor all time. The equation, as implied in FIG. 13A, can be generalizedas: ##EQU20## Equation (31) is that of an autoregressive (AR) filter or"all pole" filter.

Consider that the signal is recorded several times. Each beginning of arecording is denoted t₀. For each recording the signal is within a timewindow, which begins at time t_(c), and ends at time t₂. See FIGS. 14Aand 14B. Initially all coefficients (b₁, b₂, . . . , b_(m)) of Equation(31) are equal to zero outside of this time window. Also the signal,while maintaining an anticipated waveform, varies slightly per eachrecording, as well as varying slowly within the time window. See FIG.14A. Thus the coefficients cannot be specified a priori. thecoefficients are all zero at t_(c), then: ##EQU21## Instead ##EQU22##This is the error filter approach. The coefficients may be adjusted as:

    b.sub.j (n--1)=b.sub.j (n)+μx(n--j)ε.sub.n      (34)

where μ is an adaptation time constant. After a certain number of timeslices, the coefficient values approach their actual value for aspecific scenario, and the error approaches zero. This adaptive ARmethod is mathematically appealing, since resolving the coefficientsoccurs by means of solving a simple quadratic equation.

Now, consider a series of recordings in which the signal is as describedabove in Equations (32) through (34), except that now for theserecordings some type of noise is added. This noise could be justbackground noise, as depicted in FIG. 14B, or the noise could be aninterfering signal plus background noise. Following the lineardifference procedure which resulted in Equation (31), the realizedmathematical filter for developing FIG. 14B is: ##EQU23## where a_(k)are the coefficients of the background noise and/or an interferingsignal, W_(n--k) are the force functions of the moving average (MA)portion=X_(n--k) when t₂ <t<t_(c). Equation (35) represents theautoregressive moving average (ARMA) Model. This SAVE filter can betransitioned into an error filter, but the equation is not a simplequadratic, as in the case of the adaptive AR filters discussed above.

Referring further now to FIG. 13B, there is shown a circuit diagramillustrating the self adaptive and variant error (SAVE) filter forlearning a new pattern of deviations of signals from a previouslylearned pattern.

The self adaptive and variant error (SAVE) filter 10 has an input 11 forreceiving a series of input signals x_(n) for being processed.

A delay line 12 receives the input signals x_(n) and consists of atleast two delay stages Z⁻¹, each representing a fixed delay related to asampling interval of the signals x_(n). Each delay stage Z⁻¹ has anoutput for outputting the delayed signals x_(n--1), x_(n--2), . . . ,respectively.

An error filter multiplier 13 includes at least two multipliers, eachsuch multiplier being connected to a respective one of the outputs fromthe at least two delay stages Z³¹, as shown in FIG. 12B. Each of themultipliers in the error filter multiplier 13 generates a respectivecoefficient value a₁, a₂, . . . and calculates the respective product a₁x_(n--1), a₂ x_(n--2), . . .

The products a₁ x_(n--1), a₂ x_(n--2), . . . are output from therespective multipliers to a summer 14, which computes the sum a₁x_(n--1) -a₂ x_(n--2) - . . . The summer 14 has an output 15 that feedsthe polynomial evaluator 18 and the adaptive summer 19. Before themoment of chaos t_(c), the polynomial evaluator 18 outputs thepolynomial resulting from summer 14 only.

Prior to time t_(c), the adaptive summer 19 produces an error solelyfrom the difference between the desired response and the output fromsummer 14. That is, if there are m delays, then the error εis ##EQU24##This in turn feeds to the error generator 20.

The error generator 20 outputs the error to the coefficient adaptor 21until time t_(c). The coefficient adaptor updates the coefficients,namely

    a.sub.j+1 =a.sub.j +εμx.sub.n--j                (37)

In summary, until the moment of chaos t_(c), the unit behaves as theTRAPS filter described earlier.

At the moment of chaos detection, (1) the values of delay line 12 anderror filter multiplier 13 are "saved" for their values as mapped intothe time domain, (2) the error generator 20 feeds the error ε into thecoefficient adaptor 22, and 3) the polynomial evaluator allows B(z) tobe other than 1.

FIG. 3C presents the SAVE filter in standard adaptive filternomenclature. The noise identifier 21 comprises delay line 12, errorfilter multiplier 13, and summer 14. The signal identifier 32 comprisesdelay line 15, error filter multiplier 16, and summer 7. The polynomialevaluator 18, the adaptive summer 19, and the error generator 20 are thesame as in FIG. 13B.

Ultimately, the SAVE filter generates a new polynomial expansion patternR_(N).sbsb.i, which is then provided to the "fractal monitor" neuralnetwork for further analysis as described below.

The "Fractal Monitor" Neural Network

The "fractal monitor" neural network unit 4 will now be explained withreference to FIG. 15. The filtered data signals S_(1A), . . . , S_(NX),each with its respective identified moment of chaos, t_(c).sbsb.1A, . .. , t_(c).sbsb.NX, the pre-moment of chaos patterns P_(NX), therepresentative patterns R_(N).sbsb.i of the enhanced post-moment ofchaos data signals, and the discriminant signals D_(1n), . . . , D_(Nn)are sent to a pattern recognition system unit 4. For simplicity,R_(N).sbsb.i is used to refer to the output of the SAVE filter and/orthe TRAPS filter. The pattern recognition system learns adaptively fromexperience and separates various discriminants from the unrelated orattenuating factors.

The unit 4 consists of at least one, preferably more than one, and mostpreferably three, pattern recognition weighting units W₁, W₂, W₃, . . .W_(M), at least one of which generalizes the signal patternR_(N).sbsb.i, for i>0, in a pattern vault and chooses the stored patternR_(NX) which most easily generalizes to the signal pattern R_(N).sbsb.iand assigns weights W_(NX) to the precision of the fit of R_(N).sbsb.ito R_(NX).

The fractal monitor neural network unit 4 also utilizes storedinformation. Specifically, signal patterns R representing actual eventsin the stock market based on actual experiences are stored in a datavault (not shown) for accession by the fractal monitor neural networkunit 4. These patterns R are stored in the data vault prior to operationof the system. They may be stored in a read-only memory (ROM).Alternately, if it is desired to update the collection of patterns Rstored in the data vault based on the R_(N).sbsb.i generated by actualexperience with the system, some other form of memory device, such as arandom-access memory (RAM), may be used.

Each of the at least one weighting units W₁, . . . , W_(M) receives thenew pattern R_(N).sbsb.i of deviations calculated by the SAVE filter 10and accesses the collection of patterns R stored in the data vault. Eachweighting unit W₁, . . . , W_(M) tries to match the pattern R_(N).sbsb.ito one or more of the stored patterns R to determine what "event" ishappening to the particular stock. Each weighting unit W₁, . . . W_(M)assigns a weight W_(NXM) to the fit of the R_(N).sbsb.i to each storedpattern R and then selects the pattern R_(NX) which best matches thepattern R_(N).sbsb.i. Each weighting unit W₁, . . . , W_(M) uses adifferent technique to choose a best fit stored pattern R_(NX).

In the exemplary embodiment of FIG. 15, weighting unit W₁ is an entropicdifferential weighting unit which looks for the R which has the leastnumber of spontaneous changes or differences from R_(N). Weighting unitW₁ computes ##EQU25## Weighting unit W₁ will then choose R_(NX) tominimize W_(NX) (R) and output the selected pattern R_(NX1) togetherwith the weight W_(NX1). The weighting by unit W₁ may be done withand/or without self-similarity matching.

Self-similarity matching is discussed at length in A. Lindenmayer & P.Rusinkiewicz, The Algorithmic Beauty of Plants (Springer-Verlag, NewYork: 1990), in particular in Chapter 8, which is hereby incorporated byreference in its entirety.

In line with the techniques presented in the cited reference, the entiresignal, as seen in FIG. 14A or 14B, from point t₀ to t₂ is scanned for asimilarity match to a subset signal. If a similarity is found ormatched, the event to which the similarity is found must match thedecision made for the decision to be accepted.

Weighting unit W₂ in the exemplary embodiment of FIG. 15 is an adaptivepattern recognition weighting unit which determines how easily it is togeneralize R_(N).sbsb.i to R. Weighting unit W operates by performing aregression on R_(N).sbsb.i to try to generate each R_(NX) stored in thedata vault. Unit W₂ assigns a weight W_(NX) (R) to each R based on thenumber of iterations necessary to regress R_(N).sbsb.i to R. Because ofthe presence of Rs which do not correlate with R_(N).sbsb.i, it isdesirable to set a maximum number of iterations (and hence a maximumweight), for example, at 100 or 200. After attempting to generalizeR_(N).sbsb.i to each R in the vault, unit W₂ will select the R_(NX)which minimizes W_(NX) (R), i.e., requires the fewest iterations, andoutput the selected pattern R_(NX2) together with the weight W_(NX2).Weighting unit W₂ may use an adaptive pattern recognition filter such asthe TRAPS filter discussed above, or some other form of neural network.Again, as in the case of unit W₁, unit W₂ may perform weighting withand/or without self-similarity matching, as described above.

In the exemplary embodiment of FIG. 15, weighting unit W₃ assignsweights based on the error between the actual signal and the predictedvalue of the signal. Thus, weighting unit W₃ computes the next predictedvalue S_(NX) (R) for each stored pattern R and compares it to-the actualvalue S_(NX) to compute the error ε(R)=S_(NX) -S_(NX) (R). Unit W₃assigns a weight W_(NX) (R) to each R based on the error ε(R). Unit W₃will select the R_(NX) which minimizes W_(NX) (R), i.e. has the smallesterror ε(R), and output the selected pattern R_(NX3) together with theweight W_(NX3).

It should also be noted that this system presently describes a matchingbased on point-to-point perspective. In some uses of the invention, thematching of these patterns is improved with a compression of either thegenerated pattern or the vault pattern. This compression can be donewithout loss of meaning, for example, as described in B. Schnitta-Israeland R. S. Freedman, "Increasing the Usefulness of AWACS DownlinkTransmissions by Semantic Compression," AGARD Conference Proceedings No.414 (1986), which is hereby incorporated by reference in its entirety.

The Expert System

The resultant chosen patterns R_(NXM) and their associated weightsW_(NXM) are input to an expert system ranking unit 5. The expert systemranking unit 5 prioritizes the patterns R_(NXM) by weight W_(NXM) andhence facilitates identification of the buy/sell actions that give theuser the most potential for profitable actions. The selected actions canbe relatively prioritized and output to an output device (not shown).

If desired, the expert system ranking unit 5 can be provided with apreprogrammed set of "rules" based on actual experiences with stocks.The expert system, using a set of rules, determines, given the RNX_(M)and WNX_(M), which signals qualify for any specified category C_(f) andassigns a composite weight W_(f) to the category C_(f). In the preferredembodiment, these could be stocks for buy or sell. These outputsoptionally may be processed through a further ranking unit whichrearranges the categories C_(f) according to R_(NXM), W_(NXM), andW_(f), using, but not limited to, such a strategy as case-basedreasoning, which generalizes rules. For example, if experience showsthat "quick ups" are more volatile, shorter-lived, and harder to predictthan "quick downs" the "rules" may dictate that actions based on "quickdowns" outrank (take priority over) actions based on "quick ups." Theresults are output to an output device (not shown).

                  TABLE 1                                                         ______________________________________                                        COMPANY    SYMBOL    TIME    PATTERN WEIGHT                                   ______________________________________                                        PFIZER INC PFE       13      general up                                                                            55.11                                                         13      up      80.64                                                          3      strong up                                                                             46.26                                                          3      down    55.88                                    MEDICAL 21 MEDC      13      general up                                                                            56.24                                    CORP                 13      up      104.80                                                         3      general up                                                                            49.62                                                          3      up      87.39                                    SYNCOR INTL                                                                              SCOR      13      general up                                                                            64.03                                    CORP                 13      up      64.03                                                          3      general up                                                                            43.76                                                          3      up      43.76                                    ______________________________________                                    

Three examples, based on actual output, are shown in Table 1. The timeof 13 represents the time of the event (moment of chaos t_(c)) based ondata signals passed through the nonadaptive noise filter F₂ to removemarket noise, but not industry noise. The time of 3 represents the timeof certainty of the event (see FIGS. 12A and 12B) based on data signalspassed through the adaptive noise filter F₃ to remove industry noise.The patterns represent the patterns R_(NX) chosen from the vaultpatterns R by the weighting unit, and the weights represent the weightsW_(NX) determined by the weighting unit. In these examples, the fourbest fits were chosen without regard to which technique was used by theweighting unit.

These examples have not been processed by the expert system. As anexample of how they might be so processed, the expert system could beprogrammed with a series of rules. For example, one rule might be toreject any stock where the four best fits are not all "up" or all"down." Using this rule, PFIZER would be rejected by the expert system.Another rule might be to reject any stock where the weights of the fourbest fits are not all within specified limits. For example, for thefirst two entries using data from the nonadaptive noise filter, thelimits might be a maximum of 70 for the best fit and 100 for the secondbest fit. For the last two entries using data from the adaptive noisefilter, the limits might be a maximum of 50 for the best fit and 75 forthe second best fit. Using this rule, MEDICAL would be rejected by theexpert system. Thus, the expert system could be programmed simply toapply the desired rules and output only those stocks which meet all therules, in this example, only SYNCOR.

Preferably, the expert system utilizes a sophisticated rule-basedsystem, such as case-based reasoning, that can generalize rules. Forexample, after identifying SYNCOR as a stock of interest, the expertsystem would be able to use fuzzy logic to consider how good the fit wasto a specific pattern and what that pattern meant for that particularstock. In other words, it would learn that whenever SYNCOR matched acertain pattern in the vault, the stock price would rise significantlywithin a day and then subside. Thus, any "buy" recommendation would beoutput as a "buy immediately" recommendation (rather than a "buy forlong-term hold").

The foregoing is considered as illustrative only of the principles ofthe invention. Further, since numerous modifications and changes willreadily occur to those skilled in the art, it is not desired to limitthe invention to the exact construction and operation shown anddescribed, and accordingly, all suitable modifications and equivalentsmay be resorted to falling within the scope of the invention.

I claim:
 1. A method for determining if activity in a signal is marketactivity comprising the steps of:receiving an unfiltered signal at anoise filter unit; passing the unfiltered signal through a non-adaptivenoise filter to generate a filtered signal; passing the filtered signalthrough an adaptive pattern recognition unit; at the adaptive patternrecognition unit, predicting a first pattern that is a function of thefiltered signal; determining if there is a first deviation in thefiltered signal by comparing the filtered signal with the predictedfirst pattern; passing the unfiltered signal through the adaptivepattern recognition unit; at the adaptive pattern recognition unit,predicting a second pattern that is a function of the unfiltered signal;determining if there is a second deviation in the unfiltered signal bycomparing the unfiltered signal with the predicted second pattern; andif there exists a second deviation in the unfiltered signal and no firstdeviation in the filtered signal, outputting an indication of marketactivity.
 2. The method of claim 1 wherein the step of passing theunfiltered signal through the adaptive pattern recognition unit furthercomprises passing the unfiltered signal through a self adaptive andvariant error ("SAVE") filter.
 3. The method of claim 1 wherein the stepof passing the filtered signal through the adaptive pattern recognitionunit further comprises passing the unfiltered signal through a selfadaptive and variant error ("SAVE") filter.
 4. The method of claim 1wherein the step of passing the unfiltered signal through the adaptivepattern recognition unit further comprises passing the unfiltered signalthrough an adaptive TRAPS noise filter.
 5. The method of claim 1 whereinthe step of passing the filtered signal through the adaptive patternrecognition unit further comprises passing the unfiltered signal throughan adaptive TRAPS noise filter.
 6. A method for determining if activityin a signal is individual event activity comprising the stepsof:receiving an unfiltered signal at a noise filter unit; passing theunfiltered signal through a non-adaptive noise filter to generate afiltered signal; passing the filtered signal through an adaptive patternrecognition unit; at the adaptive pattern recognition unit, predicting afirst pattern that is a function of the filtered signal; determining ifthere is a first deviation in the filtered signal by comparing thefiltered signal with the predicted first pattern; passing the unfilteredsignal through the adaptive pattern recognition unit; at the adaptivepattern recognition unit, predicting a second pattern that is a functionof the unfiltered signal; determining if there is a second deviation inthe unfiltered signal by comparing the unfiltered signal with thepredicted second pattern; and if it is determined that there is a firstdeviation in the filtered signal and no second deviation in theunfiltered signal, outputting an indication of individual eventactivity.
 7. The method of claim 6 wherein the unfiltered signalrepresents stock prices for a plurality of stocks and the individualevent activity represents event activity for one stock.
 8. A method fordetermining if activity in a signal is market activity comprising thesteps of:receiving an unfiltered signal at a noise filter unit; passingthe unfiltered signal through an adaptive noise filter to generate afiltered signal; passing the filtered signal through an adaptive patternrecognition unit; at the adaptive pattern recognition unit, predicting afirst pattern that is a function of the filtered signal; determining ifthere is a first deviation in the filtered signal by comparing thefiltered signal with the predicted first pattern; passing the unfilteredsignal through the adaptive pattern recognition unit; at the adaptivepattern recognition unit, predicting a second pattern that is a functionof the unfiltered signal; determining if there is a second deviation inthe unfiltered signal by comparing the unfiltered signal with thepredicted second pattern; and if there exists a second deviation in theunfiltered signal and no first deviation in the filtered signal,outputting an indication of market activity.
 9. The method of claim 8wherein the step of passing the unfiltered signal through the adaptivepattern recognition unit further comprises passing the unfiltered signalthrough a self adaptive and variant error ("SAVE") filter.
 10. Themethod of claim 8 wherein the step of passing the filtered signalthrough the adaptive pattern recognition unit further comprises passingthe unfiltered signal through a self adaptive and variant error ("SAVE")filter.
 11. The method of claim 8 wherein the step of passing theunfiltered signal through the adaptive pattern recognition unit furthercomprises passing the unfiltered signal through an adaptive TRAPS noisefilter.
 12. The method of claim 8 wherein the step of passing thefiltered signal through the adaptive pattern recognition unit furthercomprises passing the unfiltered signal through an adaptive TRAPS noisefilter.
 13. A method for determining if activity in a signal isindividual event activity comprising the steps of:receiving anunfiltered signal at a noise filter unit; passing the unfiltered signalthrough an adaptive noise filter to generate a filtered signal; passingthe filtered signal through an adaptive pattern recognition unit; at theadaptive pattern recognition unit, predicting a first pattern that is afunction of the filtered signal; determining if there is a firstdeviation in the filtered signal by comparing the filtered signal withthe predicted first pattern; passing the unfiltered signal through theadaptive pattern recognition unit; at the adaptive pattern recognitionunit, predicting a second pattern that is a function of the unfilteredsignal; determining if there is a second deviation in the unfilteredsignal by comparing the unfiltered signal with the predicted secondpattern; and if it is determined that there is a first deviation in thefiltered signal and no second deviation in the unfiltered signal,outputting an indication of individual event activity.
 14. The method ofclaim 13 wherein the unfiltered signal represents stock prices for aplurality of stocks and the individual event activity represents eventactivity for one stock.
 15. The method of claim 13 wherein the step ofpassing the unfiltered signal through the adaptive pattern recognitionunit further comprises passing the unfiltered signal through a selfadaptive and variant error ("SAVE") filter.
 16. The method of claim 13wherein the step of passing the filtered signal through the adaptivepattern recognition unit further comprises passing the unfiltered signalthrough a self adaptive and variant error ("SAVE") filter.
 17. Themethod of claim 13 wherein the step of passing the unfiltered signalthrough the adaptive pattern recognition unit further comprises passingthe unfiltered signal through an adaptive TRAPS noise filter.
 18. Themethod of claim 13 wherein the step of passing the filtered signalthrough the adaptive pattern recognition unit further comprises passingthe unfiltered signal through an adaptive TRAPS noise filter.
 19. Amethod for determining a moment of chaos in a signal comprising thesteps of:passing the signal through a noise filter to generate afiltered data signal; passing the filtered data signal through anadaptive pattern recognition unit; at the adaptive pattern recognitionunit, predicting a pattern that is a function of the filtered datasignal; detecting a mathematical deviation in the filtered data signalby comparing the filtered data signal with the predicted pattern; andflagging the detected mathematical deviation as the moment of chaos. 20.The method of claim 19 wherein the noise filter is a non-adaptive noisefilter.
 21. The method of claim 19 wherein the noise filter is anadaptive TRAPS noise filter.
 22. The method of claim 19 wherein the stepof passing the filtered data signal through the adaptive patternrecognition unit further comprises passing the filtered data signalthrough a self adaptive and variant error ("SAVE") filter.
 23. Themethod of claim 19 wherein the step of passing the filtered data signalthrough the adaptive pattern recognition unit further comprises passingthe filtered data signal through an adaptive TRAPS noise filter.
 24. Amethod for analyzing a signal comprising the steps of:providing anexpert system ranking unit; passing the signal through a noise filter togenerate a filtered signal; passing the filtered signal through anadaptive pattern recognition unit; at the adaptive pattern recognitionunit, predicting a pattern that is a function of the filtered signal;detecting a mathematical deviation in the filtered signal by comparingthe filtered signal with the predicted pattern; flagging the detectedmathematical deviation; passing the filtered signal through at least twopattern recognition weighing units, the at least two pattern recognitionweighing units comprising a first pattern recognition weighing unitbeing an entopic differential weighing unit and a second patternrecognition weighing unit being an adaptive pattern recognition weighingunit; at each pattern recognition weighing unit, matching the filteredsignal at about the detected mathematical deviation with a collection ofstored patterns and assigning a weight to each stored pattern; inputtingeach stored pattern and the assigned weight of the stored pattern to theexpert system ranking unit; and at the expert system ranking unit,selecting one of the stored patterns by applying a predefined set ofgeneralized rules.
 25. The method of claim 24 wherein the noise filteris a non-adaptive noise filter.
 26. The method of claim 24 wherein thenoise filter is an adaptive TRAPS noise filter.
 27. The method of claim24 wherein the step of passing the filtered signal through the adaptivepattern recognition unit further comprises passing the filtered signalthrough a self adaptive and variant error ("SAVE") filter.
 28. Themethod of claim 24 wherein the step of passing the filtered signalthrough the adaptive pattern recognition unit further comprises passingthe filtered signal through an adaptive TRAPS noise filter.
 29. A methodfor analyzing a signal comprising the steps of:receiving an unfilteredsignal at a noise filter unit; passing the unfiltered signal through anon-adaptive noise filter to generate a first filtered data signal;passing the unfiltered signal through an adaptive TRAPS filter togenerate a second filtered data signal; passing the unfiltered signal,the first filtered data signal and the second filtered data signalthrough an adaptive pattern recognition unit; at the adaptive patternrecognition unit, predicting a first pattern that is a function of thefirst filtered data signal; at the adaptive pattern recognition unit,predicting a second pattern that is a function of the second filtereddata signal; at the adaptive pattern recognition unit, predicting athird pattern that is a function of the unfiltered signal; detecting afirst mathematical deviation in the first filtered data signal bycomparing the first filtered data signal with the first predictedpattern; detecting a second mathematical deviation in the secondfiltered data signal by comparing the second filtered data signal withthe second predicted pattern; detecting a third mathematical deviationin the unfiltered signal by comparing the unfiltered signal with thethird predicted pattern; flagging the first mathematical deviation, thesecond mathematical derivation and the third mathematical deviation;passing the first filtered data signal, the second filtered data signaland the unfiltered signal through at least two pattern recognitionweighing units; at each pattern recognition weighing unit, matching thefirst filtered data signal at about the detected first mathematicaldeviation with a collection of stored patterns and assigning a weight toeach stored pattern; at each pattern recognition weighing unit, matchingthe second filtered data signal at about the detected secondmathematical deviation with the collection of stored patterns andassigning a weight to each stored pattern; at each pattern recognitionweighing unit, matching the unfiltered signal at about the detectedthird mathematical deviation with the collection of stored patterns andassigning a weight to each stored pattern; inputting each stored patternand the assigned weights of the stored pattern to an expert systemranking unit; and at the expert system ranking unit, selecting one ofthe stored patterns by applying a predefined set of generalized rules.30. The method of claim 29 where the at least two pattern recognitionweighing units comprise a first pattern recognition weighing unit beingan entopic differential weighing unit and a second pattern recognitionweighing unit being an adaptive pattern recognition weighing unit. 31.The method of claim 29 further comprising the step of, at the expertsystem ranking unit, ordering the stored patterns by applying thepredefined set of generalized rules.
 32. The method of claim 29 whereinthe step of matching the first filtered data signal at about thedetected first mathematical deviation with the collection of storedpatterns further comprises the step of matching the first filtered datasignal at about the detected first mathematical deviation with thecollection of stored patterns using self similarity matching.
 33. Themethod of claim 29 wherein the step of matching the second filtered datasignal at about the detected second mathematical deviation with acollection of stored patterns further comprises the step of matching thesecond filtered data signal at about the detected second mathematicaldeviation with the collection of stored patterns using self similaritymatching.
 34. The method of claim 29 wherein the step of matching theunfiltered data signal at about the detected third mathematicaldeviation with a collection of stored patterns further comprises thestep of matching the unfiltered data signal at about the detected thirdmathematical deviation with the collection of stored patterns using selfsimilarity matching.
 35. A method for analyzing a signal representingstock prices comprising the steps of:receiving, at a noise filter unit,a signal representative of the price of stocks over time; passing thesignal through a noise filter to generate a filtered signal; passing thefiltered signal through an adaptive pattern recognition unit; at theadaptive pattern recognition unit, predicting a pattern that is afunction of the filtered signal; detecting a mathematical deviation inthe filtered signal by comparing the filtered signal with the predictedpattern; and flagging the detected mathematical deviation; passing thefiltered signals through at a pattern recognition weighing unit; at thepattern recognition weighing unit, matching the filtered signal at aboutthe detected mathematical deviation with a collection of storedpatterns; and at the pattern recognition unit, determining, for aparticular stock, whether the stock is in an uptrend or in a downtrend.